AI continues to emerge as one of the fastest-growing technologies in modern business, offering powerful opportunities to transform industries, streamline operations, and accelerate innovation. At the same time, however, the rapid adoption of AI has introduced new security gaps, operational risks, and complex vulnerabilities that organizations can no longer afford to overlook. As enterprises increasingly integrate AI into daily workflows, safeguarding these systems has become a critical priority.
According to McKinsey, 78% of organizations now use AI in at least one business scenario, a notable rise from 55% just two years ago. In parallel, the Thales 2025 Data Threat Report reveals that 73% of organizations have already started investing in AI-specific security tools, either through new budgets or by reallocating existing resources. These figures clearly highlight a growing awareness that innovation without security can expose enterprises to significant financial and reputational damage.
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Against this backdrop, Thales has launched the initial core capabilities of its AI Security Fabric, designed to deliver essential protection across both the core and edge of enterprise AI environments. With this launch, Thales directly addresses the pressing need for security frameworks purpose-built for modern AI ecosystems.
Through the Thales AI Security Fabric, enterprises can securely unlock the growth potential of AI while maintaining strong risk controls. As organizations continue to innovate and scale AI-driven initiatives, the platform actively reduces threats such as prompt injection, data leakage, model tampering, and the exposure of sensitive or regulated information. As a result, businesses can maximize the commercial value of AI without compromising trust or compliance.
In addition, the solution provides end-to-end protection for data, applications, and identity. It enables controlled access to datasets used by agent-based and generative AI, supports runtime security across both cloud and on-premises environments, and secures every AI interaction with minimal integration effort. Moreover, by leveraging enterprise-grade and standards-compliant security capabilities, Thales helps organizations directly address the OWASP Top 10 Critical Risks, thereby reducing the likelihood of costly security incidents before they occur.
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The first set of released features further strengthens this approach. AI Application Security Protection delivers real-time defense for enterprise-developed LLM applications, protecting against threats such as prompt injection, jailbreak attempts, system prompt leakage, denial-of-service attacks, sensitive data exposure, and content security violations. Notably, the solution supports flexible deployment across cloud-native, on-premises, and hybrid environments. Additionally, AI Search Enhancement Generation (RAG) Security uses advanced encryption and key management to identify and protect sensitive data before it enters search-enhanced AI applications, while also securing communication between LLMs and external data sources.
Sebastien Cano, Senior Vice President of Cybersecurity Products at Thales, stated, “As AI reshapes business models, organizations urgently need security solutions specifically designed to address the specific risks posed by agentic and generative AI applications. Thales AI Security Fabric provides enterprises with dedicated tools to ensure the security of AI applications while minimizing operational complexity. Leveraging decades of security expertise, Thales helps enterprises more easily scale their AI applications, protecting sensitive data, applications, and user interactions.”
Looking ahead, Thales plans to expand the AI Security Fabric in 2026 by introducing advanced runtime security capabilities, including data breach protection, a Model Context Protocol (MCP) security gateway, and end-to-end runtime access control. These enhancements will further strengthen data flow protection, ensure secure access to proxy AI data, and enable unified, compliant management of interactions between users, models, and data sources.
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